Transdimensional sequential Monte Carlo using variational Bayes — SMCVB

A new transdimensional Sequential Monte Carlo (SMC) algorithm called SM- CVB is proposed. In an SMC approach, a weighted sample of particles is generated from a sequence of probability distributions which ‘converge’ to the target distribution of interest, in this case a Bayesian posterior distri- bution. The approach is based on the use of variational Bayes to propose new particles at each iteration of the SMCVB algorithm in order to target the posterior more efficiently. The variational-Bayes-generated proposals are not limited to a fixed dimension. This means that the weighted particle sets that arise can have varying dimensions thereby allowing us the option to also estimate an appropriate dimension for the model. This novel algorithm is outlined within the context of finite mixture model estimation. This pro- vides a less computationally demanding alternative to using reversible jump Markov chain Monte Carlo kernels within an SMC approach. We illustrate these ideas in a simulated data analysis and in applications.

DIY/DIWO media architecture: The InstaBooth

Building on previous discourse regarding the ability of media architecture to be more open and accessible for the purposes of community engagement (Caldwell & Foth, 2014), this chapter explores a particular case study that was designed, constructed and implemented with the intention of allowing city users to participate in the development and creation of media architecture, the InstaBooth. In this chapter, we first explore DIY (do it yourself) and DIWO (do it with others) phenomena to examine what motivates the DIY cultures, communities, and practices. Secondly, in this chapter, we define and discuss our implementation of a DIY / DIWO media architecture example, the InstaBooth. The InstaBooth project pro-vides an opportunity to question the effectiveness of a DIY driven media architec-ture artefact to see to what extent it impacts on the experience of its users and for what benefit.